Forecasting value-at-risk for cryptocurrencies

IF 1.8 4区经济学 Q2 BUSINESS, FINANCE

International Review of Finance Pub Date : 2025-07-14 DOI:10.1111/irfi.70029

Michael Michaelides, Niraj Poudyal

引用次数: 0

Abstract

Value-at-Risk (VaR), the primary measure of downside risk in market risk management, relies heavily on the accuracy of volatility forecasts produced by risk models. This paper shows that, for forecasting the VaR of cryptocurrencies, the time-heterogeneous Student's t autoregressive model outperforms standard models commonly used by practitioners.

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预测加密货币的风险价值

风险价值（VaR）是市场风险管理中衡量下行风险的主要指标，在很大程度上依赖于风险模型所产生的波动率预测的准确性。本文表明，对于预测加密货币的VaR，时间异构的学生t自回归模型优于从业者常用的标准模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Review of Finance BUSINESS, FINANCE-

CiteScore

3.30

自引率

5.90%

发文量

期刊介绍： The International Review of Finance (IRF) publishes high-quality research on all aspects of financial economics, including traditional areas such as asset pricing, corporate finance, market microstructure, financial intermediation and regulation, financial econometrics, financial engineering and risk management, as well as new areas such as markets and institutions of emerging market economies, especially those in the Asia-Pacific region. In addition, the Letters Section in IRF is a premium outlet of letter-length research in all fields of finance. The length of the articles in the Letters Section is limited to a maximum of eight journal pages.